Discourse strategies for generating natural-language text
Artificial Intelligence
Expressibility and the Problem of Efficient Text Planning
Expressibility and the Problem of Efficient Text Planning
A Model for Adapting Explanations to the User‘s Likely Inferences
User Modeling and User-Adapted Interaction
Translating Machine-Generated Resolution Proofs into ND-Proofs at the Assertion Level
PRICAI '96 Proceedings of the 4th Pacific Rim International Conference on Artificial Intelligence: Topics in Artificial Intelligence
Proceedings of the 6th International Workshop on Natural Language Generation: Aspects of Automated Natural Language Generation
Presenting Machine-Found Proofs
CADE-13 Proceedings of the 13th International Conference on Automated Deduction: Automated Deduction
Omega: Towards a Mathematical Assistant
CADE-14 Proceedings of the 14th International Conference on Automated Deduction
Planning text for advisory dialogues: capturing intentional and rhetorical information
Computational Linguistics
A hybrid reasoning model for indirect answers
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Generating inference-rich discourse through revisions of RST-Trees
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Presenting Proofs in a Human-Oriented Way
CADE-16 Proceedings of the 16th International Conference on Automated Deduction: Automated Deduction
Presenting Mathematical Concepts as an Example for Inference-Rich Domains
NLDB '00 Proceedings of the 5th International Conference on Applications of Natural Language to Information Systems-Revised Papers
Presenting inequations in mathematical proofs
Information Sciences: an International Journal
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Proof presentation systems and, in some more general context, many natural language generation systems suffer from a crucial problem: they present too much information explicitly which the intended audience could more naturally infer from a less detailed text. Moreover, proofs in mathematical textbooks make extensive use of building chains of inferences in specialized notations, which is not sufficiently taken into account by proof presentation systems. Encouraged by these observations, we present a model for presenting mathematical proofs that (1) features the implicit conveyance of information through concise texts, (2) organizes major lines in the proof presentation around focused chains of inferences in a specialized notation, (3) can adapt its output to some of the capabilities of its audience. The methods described in this paper allow us to present proofs of moderately complex size in a quality approaching that of proofs found in mathematical textbooks.